In the present days, Micro blogging has become a very popular communication tool among Internet users. Many users share tweets or messages everyday on prevalent sites, for example, Twitter and Facebook. Authors of those messages write about their life, share opinions on variety of topics and discuss current issues.
As more and more users post about products and services they use, or express their political and religious views, micro blogging web-sites become valuable sources of people‟s opinions and sentiments. Such data can be efficiently used in research, business or social science.
Sentiment is positive or negative reviews about product or on any topics. We people can identify tweets by reading whether it is positive or negative. But if there is huge data to be read then it would be tedious and time consuming. So, if all this process could be done with the help of automated program then it would be easier and above manual process could be eliminated.
Sentiment Analysis is a method for judging somebody's sentiment or feeling with respect to a specific thing written in a piece of text. It is used to recognize and arrange the sentiments communicated in writings. The web-based social networking sites like twitter draws in a huge number of clients that are online for imparting their insights in the form of tweets or comments. The tweets can be then classified into positive, negative, or neutral. In the proposed work, logistic regression classification is used as a classifier and unigram as a feature vector.
Sentiment Analysis of Twitter Data Using Logistic Regression is a web-based application which takes tweets as an input and gives sentiment value as an output.